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| import numpy as np | |
| import h5py | |
| import cv2 | |
| def normalize_intrinsic(x, K): | |
| # print(x,K) | |
| return (x - K[:2, 2]) / np.diag(K)[:2] | |
| def normalize_size(x, size, scale=1): | |
| size = size.reshape([1, 2]) | |
| norm_fac = size.max() | |
| return (x - size / 2 + 0.5) / (norm_fac * scale) | |
| def np_skew_symmetric(v): | |
| zero = np.zeros_like(v[:, 0]) | |
| M = np.stack( | |
| [ | |
| zero, | |
| -v[:, 2], | |
| v[:, 1], | |
| v[:, 2], | |
| zero, | |
| -v[:, 0], | |
| -v[:, 1], | |
| v[:, 0], | |
| zero, | |
| ], | |
| axis=1, | |
| ) | |
| return M | |
| def draw_points(img, points, color=(0, 255, 0), radius=3): | |
| dp = [(int(points[i, 0]), int(points[i, 1])) for i in range(points.shape[0])] | |
| for i in range(points.shape[0]): | |
| cv2.circle(img, dp[i], radius=radius, color=color) | |
| return img | |
| def draw_match( | |
| img1, | |
| img2, | |
| corr1, | |
| corr2, | |
| inlier=[True], | |
| color=None, | |
| radius1=1, | |
| radius2=1, | |
| resize=None, | |
| ): | |
| if resize is not None: | |
| scale1, scale2 = [img1.shape[1] / resize[0], img1.shape[0] / resize[1]], [ | |
| img2.shape[1] / resize[0], | |
| img2.shape[0] / resize[1], | |
| ] | |
| img1, img2 = cv2.resize(img1, resize, interpolation=cv2.INTER_AREA), cv2.resize( | |
| img2, resize, interpolation=cv2.INTER_AREA | |
| ) | |
| corr1, corr2 = ( | |
| corr1 / np.asarray(scale1)[np.newaxis], | |
| corr2 / np.asarray(scale2)[np.newaxis], | |
| ) | |
| corr1_key = [ | |
| cv2.KeyPoint(corr1[i, 0], corr1[i, 1], radius1) for i in range(corr1.shape[0]) | |
| ] | |
| corr2_key = [ | |
| cv2.KeyPoint(corr2[i, 0], corr2[i, 1], radius2) for i in range(corr2.shape[0]) | |
| ] | |
| assert len(corr1) == len(corr2) | |
| draw_matches = [cv2.DMatch(i, i, 0) for i in range(len(corr1))] | |
| if color is None: | |
| color = [(0, 255, 0) if cur_inlier else (0, 0, 255) for cur_inlier in inlier] | |
| if len(color) == 1: | |
| display = cv2.drawMatches( | |
| img1, | |
| corr1_key, | |
| img2, | |
| corr2_key, | |
| draw_matches, | |
| None, | |
| matchColor=color[0], | |
| singlePointColor=color[0], | |
| flags=4, | |
| ) | |
| else: | |
| height, width = max(img1.shape[0], img2.shape[0]), img1.shape[1] + img2.shape[1] | |
| display = np.zeros([height, width, 3], np.uint8) | |
| display[: img1.shape[0], : img1.shape[1]] = img1 | |
| display[: img2.shape[0], img1.shape[1] :] = img2 | |
| for i in range(len(corr1)): | |
| left_x, left_y, right_x, right_y = ( | |
| int(corr1[i][0]), | |
| int(corr1[i][1]), | |
| int(corr2[i][0] + img1.shape[1]), | |
| int(corr2[i][1]), | |
| ) | |
| cur_color = (int(color[i][0]), int(color[i][1]), int(color[i][2])) | |
| cv2.line( | |
| display, | |
| (left_x, left_y), | |
| (right_x, right_y), | |
| cur_color, | |
| 1, | |
| lineType=cv2.LINE_AA, | |
| ) | |
| return display | |